Don’t use LLMs to grade student work’. Here’s why :

As the excitement around AI continues to build, it’s tempting to throw the latest shiny tool at every problem, especially in education. With models like ChatGPT and Gemini dazzling us with their ability to generate fluent, human-like responses, it’s natural to ask,

At Graide, we’ve explored this question deeply. And the answer, grounded in both technical insight and ethical responsibility, is clear.

You shouldn’t use LLMs to grade.

LLMs aren’t just smarter autograders; they are something entirely different.

To understand why, it helps to distinguish between two broad categories of AI: classification models and generative models.

Classification models, the kind we champion at Graide, are designed to evaluate. They learn from examples, categorise new data and provide interpretable results. They are trained, tested and implemented to say things like, “This essay demonstrates critical thinking,” or “This response lacks evidence.” You can benchmark them, audit them and, most importantly, you can trust them.

Generative models, on the other hand, are built to create. They’re the poets of the AI world. They are experts in spinning out text, completing sentences and even writing code. But ask them to decide whether a student deserves a B+ or a C, and things start to unravel. Why? Because their inner workings are not grounded in truth but in probability. LLMs tend to fall under this generative AI bucket.

Grading isn’t just a task; it’s a high-stakes, high-risk decision.

Grading is not a mechanical checkbox activity. It’s a nuanced process that affects student confidence, educational outcomes and often, future opportunities. Mistakes are not just frustrating but consequential.

LLMs introduce this risk in three ways:

— They hallucinate, confidently inventing facts or reasoning that never existed.

–They lack consistency; the same answer today may not get the same grade tomorrow.

–They are opaque even when they are wrong; it’s hard to understand why.

Imagine a student appealing their grade, and your only explanation is, “That is what the model said.” That’s not just bad pedagogy. It’s bad governance.

The Bottom Line

LLMs have a place in education for ideation, tutoring, draughting, and even generating educational content. But grading is different. It requires structure, explainability, fairness and fidelity to learning goals.

That is why at Graide, we don’t use LLMs. We use classification AI, built with guardrails, trained for your context and evaluated like a human marker would be.

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